Overview

Dataset statistics

Number of variables14
Number of observations52560
Missing cells702
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric13

Alerts

Power (kW) is highly overall correlated with blade_angle and 7 other fieldsHigh correlation
Wind direction (°) is highly overall correlated with Nacelle position (°)High correlation
Nacelle position (°) is highly overall correlated with Wind direction (°)High correlation
blade_angle is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Rear bearing temperature (°C) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Rotor speed (RPM) is highly overall correlated with Power (kW) and 7 other fieldsHigh correlation
Generator RPM (RPM) is highly overall correlated with Power (kW) and 7 other fieldsHigh correlation
Front bearing temperature (°C) is highly overall correlated with Power (kW) and 7 other fieldsHigh correlation
Tower Acceleration X (mm/ss) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Wind speed (m/s) is highly overall correlated with Power (kW) and 7 other fieldsHigh correlation
Tower Acceleration y (mm/ss) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
# Date and time has unique valuesUnique
blade_angle has 18491 (35.2%) zerosZeros
Rotor speed (RPM) has 1304 (2.5%) zerosZeros

Reproduction

Analysis started2023-07-08 11:54:26.399787
Analysis finished2023-07-08 11:54:43.687192
Duration17.29 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

# Date and time
Date

Distinct52560
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size410.8 KiB
Minimum2019-01-01 00:00:00
Maximum2019-12-31 23:50:00
2023-07-08T17:24:43.855078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:43.947851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Power (kW)
Real number (ℝ)

Distinct52244
Distinct (%)99.5%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean493.2504
Minimum-16.118205
Maximum2086.9265
Zeros1
Zeros (%)< 0.1%
Negative6541
Negative (%)12.4%
Memory size410.8 KiB
2023-07-08T17:24:44.048894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-16.118205
5-th percentile-2.0517077
Q184.636049
median295.32143
Q3702.42735
95-th percentile1822.4038
Maximum2086.9265
Range2103.0447
Interquartile range (IQR)617.7913

Descriptive statistics

Standard deviation545.65553
Coefficient of variation (CV)1.1062445
Kurtosis1.0524953
Mean493.2504
Median Absolute Deviation (MAD)257.99695
Skewness1.3837347
Sum25898605
Variance297739.95
MonotonicityNot monotonic
2023-07-08T17:24:44.143742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.58289454 5
 
< 0.1%
-1.621840042 4
 
< 0.1%
-1.664520043 4
 
< 0.1%
-1.116615531 4
 
< 0.1%
-1.660785544 4
 
< 0.1%
-1.165164033 4
 
< 0.1%
-0.8962800264 4
 
< 0.1%
-1.61277054 3
 
< 0.1%
-1.677857536 3
 
< 0.1%
-1.75468154 3
 
< 0.1%
Other values (52234) 52468
99.8%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
-16.11820512 1
< 0.1%
-15.87122407 1
< 0.1%
-15.5536231 1
< 0.1%
-14.90161648 1
< 0.1%
-14.61988831 1
< 0.1%
-14.3402589 1
< 0.1%
-14.03994415 1
< 0.1%
-14.01185151 1
< 0.1%
-13.95797803 1
< 0.1%
-13.77005856 1
< 0.1%
ValueCountFrequency (%)
2086.926514 1
< 0.1%
2074.198065 1
< 0.1%
2074.109521 1
< 0.1%
2073.62426 1
< 0.1%
2073.526855 1
< 0.1%
2073.514612 1
< 0.1%
2073.169952 1
< 0.1%
2073.073676 1
< 0.1%
2073.052039 1
< 0.1%
2072.264038 1
< 0.1%

Wind direction (°)
Real number (ℝ)

Distinct52505
Distinct (%)> 99.9%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean204.23556
Minimum0.019831259
Maximum359.98319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:44.240623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.019831259
5-th percentile29.976101
Q1148.60041
median218.09321
Q3269.02131
95-th percentile332.34191
Maximum359.98319
Range359.96335
Interquartile range (IQR)120.42091

Descriptive statistics

Standard deviation90.249037
Coefficient of variation (CV)0.44188698
Kurtosis-0.54487365
Mean204.23556
Median Absolute Deviation (MAD)57.698401
Skewness-0.5312701
Sum10723593
Variance8144.8887
MonotonicityNot monotonic
2023-07-08T17:24:44.339191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
314.9356995 2
 
< 0.1%
280.7239685 1
 
< 0.1%
275.7419948 1
 
< 0.1%
280.8380058 1
 
< 0.1%
276.113653 1
 
< 0.1%
269.2994493 1
 
< 0.1%
276.928398 1
 
< 0.1%
271.2884071 1
 
< 0.1%
266.6303538 1
 
< 0.1%
274.7991891 1
 
< 0.1%
Other values (52495) 52495
99.9%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
0.01983125908 1
< 0.1%
0.02119049977 1
< 0.1%
0.02634646709 1
< 0.1%
0.03665571259 1
< 0.1%
0.0381845716 1
< 0.1%
0.04547579657 1
< 0.1%
0.05247045693 1
< 0.1%
0.05546216763 1
< 0.1%
0.06654265523 1
< 0.1%
0.06957325449 1
< 0.1%
ValueCountFrequency (%)
359.9831852 1
< 0.1%
359.9739899 1
< 0.1%
359.9509525 1
< 0.1%
359.9484721 1
< 0.1%
359.9388549 1
< 0.1%
359.9350615 1
< 0.1%
359.9259612 1
< 0.1%
359.8989129 1
< 0.1%
359.8907467 1
< 0.1%
359.88765 1
< 0.1%

Nacelle position (°)
Real number (ℝ)

Distinct14183
Distinct (%)27.0%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean204.08711
Minimum0.039325053
Maximum359.97885
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:44.441646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.039325053
5-th percentile27.655449
Q1148.10595
median218.63123
Q3268.51981
95-th percentile333.27321
Maximum359.97885
Range359.93953
Interquartile range (IQR)120.41386

Descriptive statistics

Standard deviation90.579879
Coefficient of variation (CV)0.4438295
Kurtosis-0.54136201
Mean204.08711
Median Absolute Deviation (MAD)57.073242
Skewness-0.53652578
Sum10715798
Variance8204.7145
MonotonicityNot monotonic
2023-07-08T17:24:44.537641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
316.315094 130
 
0.2%
244.9728699 117
 
0.2%
250.4598083 103
 
0.2%
52.89989853 102
 
0.2%
215.337738 100
 
0.2%
26.55852127 96
 
0.2%
37.53403091 95
 
0.2%
248.2660217 95
 
0.2%
252.6549377 94
 
0.2%
197.777832 94
 
0.2%
Other values (14173) 51480
97.9%
ValueCountFrequency (%)
0.03932505287 1
 
< 0.1%
0.09177728818 1
 
< 0.1%
0.1622003148 1
 
< 0.1%
0.2034744711 1
 
< 0.1%
0.2163391113 3
 
< 0.1%
0.2163393497 5
 
< 0.1%
0.2164611816 5
 
< 0.1%
0.2165527344 1
 
< 0.1%
0.2165534496 5
 
< 0.1%
0.2165839672 17
< 0.1%
ValueCountFrequency (%)
359.9788509 1
< 0.1%
359.9749507 1
< 0.1%
359.9747647 1
< 0.1%
359.9431253 1
< 0.1%
359.8387555 1
< 0.1%
359.8294603 1
< 0.1%
359.8134288 1
< 0.1%
359.7907213 1
< 0.1%
359.7817016 1
< 0.1%
359.7604226 1
< 0.1%

blade_angle
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20904
Distinct (%)39.8%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6.4633095
Minimum0
Maximum92.596667
Zeros18491
Zeros (%)35.2%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:44.640685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.19733304
Q31.4183355
95-th percentile44.993334
Maximum92.596667
Range92.596667
Interquartile range (IQR)1.4183355

Descriptive statistics

Standard deviation15.610709
Coefficient of variation (CV)2.4152811
Kurtosis8.1247526
Mean6.4633095
Median Absolute Deviation (MAD)0.19733304
Skewness2.8146346
Sum339362.53
Variance243.69424
MonotonicityNot monotonic
2023-07-08T17:24:44.732725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18491
35.2%
44.99333445 4041
 
7.7%
1.49333334 1243
 
2.4%
0.02466640632 925
 
1.8%
0.04933289496 356
 
0.7%
44.99666723 356
 
0.7%
0.07399945676 218
 
0.4%
1.49666667 213
 
0.4%
0.4933333397 171
 
0.3%
89.99333191 165
 
0.3%
Other values (20894) 26327
50.1%
ValueCountFrequency (%)
0 18491
35.2%
0.0001666666552 1
 
< 0.1%
0.0001666666622 13
 
< 0.1%
0.0001851851897 1
 
< 0.1%
0.0003333333104 1
 
< 0.1%
0.0003333333201 5
 
< 0.1%
0.0003333333244 22
 
< 0.1%
0.000350877177 1
 
< 0.1%
0.0003508771791 1
 
< 0.1%
0.0003508771836 1
 
< 0.1%
ValueCountFrequency (%)
92.59666697 50
 
0.1%
92.49333191 12
 
< 0.1%
92.47666677 2
 
< 0.1%
92.4399999 147
0.3%
92.43466619 1
 
< 0.1%
91.92333221 1
 
< 0.1%
91.91666667 8
 
< 0.1%
91.91333516 1
 
< 0.1%
91.91333262 2
 
< 0.1%
91.91000112 1
 
< 0.1%

Rear bearing temperature (°C)
Real number (ℝ)

Distinct37562
Distinct (%)71.5%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean63.288777
Minimum13.884999
Maximum74.868421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:44.824483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13.884999
5-th percentile41.865626
Q161.845
median66.515
Q368.77
95-th percentile70.925001
Maximum74.868421
Range60.983422
Interquartile range (IQR)6.9250005

Descriptive statistics

Standard deviation9.0736771
Coefficient of variation (CV)0.14336945
Kurtosis4.735132
Mean63.288777
Median Absolute Deviation (MAD)2.8650002
Skewness-2.1368864
Sum3323040.5
Variance82.331616
MonotonicityNot monotonic
2023-07-08T17:24:44.914415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 12
 
< 0.1%
68.47249985 9
 
< 0.1%
67.89500008 9
 
< 0.1%
67.50500031 9
 
< 0.1%
68.63249969 9
 
< 0.1%
68.47749977 8
 
< 0.1%
67.21500053 8
 
< 0.1%
67.75000038 8
 
< 0.1%
66.71250038 8
 
< 0.1%
70.5 8
 
< 0.1%
Other values (37552) 52418
99.7%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
13.88499928 1
< 0.1%
13.92749977 1
< 0.1%
13.96000004 1
< 0.1%
13.96749973 1
< 0.1%
14.03750038 1
< 0.1%
14.07999992 1
< 0.1%
14.15499973 1
< 0.1%
14.17999935 1
< 0.1%
14.28250027 1
< 0.1%
14.33250046 1
< 0.1%
ValueCountFrequency (%)
74.86842145 1
< 0.1%
74.11750031 1
< 0.1%
74.10999832 1
< 0.1%
74.09249878 1
< 0.1%
74.05263078 1
< 0.1%
74.02500076 1
< 0.1%
73.97631555 1
< 0.1%
73.97249947 1
< 0.1%
73.9625 1
< 0.1%
73.94499931 1
< 0.1%

Rotor speed (RPM)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51143
Distinct (%)97.4%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean10.004959
Minimum0
Maximum15.340002
Zeros1304
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:45.011408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.51940036
Q19.1093107
median10.02576
Q312.746692
95-th percentile15.131294
Maximum15.340002
Range15.340002
Interquartile range (IQR)3.6373816

Descriptive statistics

Standard deviation3.9858605
Coefficient of variation (CV)0.3983885
Kurtosis0.89330646
Mean10.004959
Median Absolute Deviation (MAD)1.3995182
Skewness-1.1059843
Sum525320.37
Variance15.887084
MonotonicityNot monotonic
2023-07-08T17:24:45.108313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1304
 
2.5%
0.0110000018 8
 
< 0.1%
0.01150000188 8
 
< 0.1%
0.01200000197 6
 
< 0.1%
0.01050000242 5
 
< 0.1%
0.01400000229 3
 
< 0.1%
0.01050000265 3
 
< 0.1%
0.02250000369 3
 
< 0.1%
0.01600000123 3
 
< 0.1%
8.996820509 2
 
< 0.1%
Other values (51133) 51161
97.3%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
0 1304
2.5%
0.0003360000919 1
 
< 0.1%
0.001254000206 1
 
< 0.1%
0.002080000355 1
 
< 0.1%
0.006352000404 1
 
< 0.1%
0.006552001694 1
 
< 0.1%
0.007308001863 1
 
< 0.1%
0.008866001386 1
 
< 0.1%
0.01050000242 5
 
< 0.1%
0.01050000265 3
 
< 0.1%
ValueCountFrequency (%)
15.34000218 1
< 0.1%
15.32826166 1
< 0.1%
15.29539204 1
< 0.1%
15.29132128 1
< 0.1%
15.29078186 1
< 0.1%
15.27974617 1
< 0.1%
15.27901594 1
< 0.1%
15.27753621 1
< 0.1%
15.27631326 1
< 0.1%
15.2712767 1
< 0.1%

Generator RPM (RPM)
Real number (ℝ)

Distinct52492
Distinct (%)> 99.9%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1186.7236
Minimum-167.0685
Maximum1814.8151
Zeros0
Zeros (%)0.0%
Negative411
Negative (%)0.8%
Memory size410.8 KiB
2023-07-08T17:24:45.213825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-167.0685
5-th percentile60.971729
Q11081.2042
median1189.7183
Q31511.4159
95-th percentile1793.342
Maximum1814.8151
Range1981.8836
Interquartile range (IQR)430.21176

Descriptive statistics

Standard deviation472.48109
Coefficient of variation (CV)0.39813913
Kurtosis0.90119586
Mean1186.7236
Median Absolute Deviation (MAD)165.51445
Skewness-1.1111572
Sum62310107
Variance223238.38
MonotonicityNot monotonic
2023-07-08T17:24:45.309142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1752.439779 2
 
< 0.1%
1799.770508 2
 
< 0.1%
1067.595863 2
 
< 0.1%
1798.029053 2
 
< 0.1%
1068.18889 2
 
< 0.1%
1312.027245 2
 
< 0.1%
1796.673462 2
 
< 0.1%
1138.875732 2
 
< 0.1%
1102.389221 2
 
< 0.1%
1265.441219 2
 
< 0.1%
Other values (52482) 52486
99.9%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
-167.0684966 1
< 0.1%
-116.8770956 1
< 0.1%
-116.2942123 1
< 0.1%
-70.02964474 1
< 0.1%
-46.64635171 1
< 0.1%
-23.3598938 1
< 0.1%
-0.5749955708 1
< 0.1%
-0.5622841548 1
< 0.1%
-0.5610444946 1
< 0.1%
-0.5125381085 1
< 0.1%
ValueCountFrequency (%)
1814.815069 1
< 0.1%
1812.091899 1
< 0.1%
1811.652134 1
< 0.1%
1811.455004 1
< 0.1%
1811.191952 1
< 0.1%
1811.177519 1
< 0.1%
1810.772078 1
< 0.1%
1810.748291 1
< 0.1%
1810.502424 1
< 0.1%
1809.723621 1
< 0.1%

Nacelle ambient temperature (°C)
Real number (ℝ)

Distinct31514
Distinct (%)60.0%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean11.872311
Minimum0.077500001
Maximum36.914999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:45.412218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.077500001
5-th percentile3.7944443
Q17.6649998
median11.165
Q315.7825
95-th percentile21.567072
Maximum36.914999
Range36.837499
Interquartile range (IQR)8.1175002

Descriptive statistics

Standard deviation5.6193188
Coefficient of variation (CV)0.47331298
Kurtosis0.056472905
Mean11.872311
Median Absolute Deviation (MAD)3.9849997
Skewness0.50579354
Sum623367.55
Variance31.576744
MonotonicityNot monotonic
2023-07-08T17:24:45.620199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.5 93
 
0.2%
7.199999809 74
 
0.1%
8.5 74
 
0.1%
9.699999809 71
 
0.1%
6.599999905 71
 
0.1%
8.800000191 70
 
0.1%
7.900000095 69
 
0.1%
9.5 67
 
0.1%
9 64
 
0.1%
8.399999619 62
 
0.1%
Other values (31504) 51791
98.5%
ValueCountFrequency (%)
0.0775000006 1
< 0.1%
0.1449999958 1
< 0.1%
0.174999997 1
< 0.1%
0.1850000024 1
< 0.1%
0.1925000101 1
< 0.1%
0.1975000054 1
< 0.1%
0.2049999982 1
< 0.1%
0.2450000048 1
< 0.1%
0.2549999952 1
< 0.1%
0.2574999928 1
< 0.1%
ValueCountFrequency (%)
36.91499939 1
< 0.1%
36.78999996 1
< 0.1%
36.62249985 1
< 0.1%
36.60750008 1
< 0.1%
36.48157903 1
< 0.1%
36.48000031 1
< 0.1%
36.46250019 1
< 0.1%
36.41250038 1
< 0.1%
36.37000008 1
< 0.1%
36.35999966 1
< 0.1%

Front bearing temperature (°C)
Real number (ℝ)

Distinct40342
Distinct (%)76.8%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean64.918483
Minimum14.33
Maximum83.642105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:45.723957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14.33
5-th percentile41.354376
Q160.12
median68.83875
Q372.6325
95-th percentile75.39418
Maximum83.642105
Range69.312105
Interquartile range (IQR)12.5125

Descriptive statistics

Standard deviation10.790471
Coefficient of variation (CV)0.1662157
Kurtosis1.9077133
Mean64.918483
Median Absolute Deviation (MAD)4.7662491
Skewness-1.4575759
Sum3408609.9
Variance116.43426
MonotonicityNot monotonic
2023-07-08T17:24:45.819813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.29999924 11
 
< 0.1%
29 11
 
< 0.1%
74.25 9
 
< 0.1%
73.875 9
 
< 0.1%
71.87250023 8
 
< 0.1%
73.59999847 8
 
< 0.1%
72 8
 
< 0.1%
72.40999985 8
 
< 0.1%
73.05749931 8
 
< 0.1%
73.8375 7
 
< 0.1%
Other values (40332) 52419
99.7%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
14.32999992 1
< 0.1%
14.36250019 1
< 0.1%
14.43499947 1
< 0.1%
14.53999996 1
< 0.1%
14.67000008 1
< 0.1%
14.68249989 1
< 0.1%
14.69999981 2
< 0.1%
14.73999977 1
< 0.1%
14.76000023 1
< 0.1%
14.79999924 1
< 0.1%
ValueCountFrequency (%)
83.6421051 1
< 0.1%
83.50750008 1
< 0.1%
83.22500076 1
< 0.1%
83.09499855 1
< 0.1%
83.05500031 1
< 0.1%
82.76500053 1
< 0.1%
82.71500092 1
< 0.1%
82.67631651 1
< 0.1%
82.67500114 1
< 0.1%
82.65500107 1
< 0.1%

Tower Acceleration X (mm/ss)
Real number (ℝ)

Distinct52497
Distinct (%)> 99.9%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean70.601717
Minimum2.528162
Maximum249.76428
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:45.919571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.528162
5-th percentile4.6564034
Q146.670337
median70.803164
Q394.80263
95-th percentile133.58944
Maximum249.76428
Range247.23612
Interquartile range (IQR)48.132293

Descriptive statistics

Standard deviation37.721689
Coefficient of variation (CV)0.53428855
Kurtosis-0.12540271
Mean70.601717
Median Absolute Deviation (MAD)24.066048
Skewness0.14744823
Sum3707013.7
Variance1422.9258
MonotonicityNot monotonic
2023-07-08T17:24:46.017551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.61788177 2
 
< 0.1%
4.600774479 2
 
< 0.1%
4.63894558 2
 
< 0.1%
4.218248439 2
 
< 0.1%
77.96581268 2
 
< 0.1%
111.4313195 2
 
< 0.1%
95.43769674 2
 
< 0.1%
4.625669903 2
 
< 0.1%
70.425142 2
 
< 0.1%
126.3473358 1
 
< 0.1%
Other values (52487) 52487
99.9%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
2.528162003 1
< 0.1%
3.027783865 1
< 0.1%
3.289621585 1
< 0.1%
3.318614787 1
< 0.1%
3.3240318 1
< 0.1%
3.335994482 1
< 0.1%
3.355663276 1
< 0.1%
3.365514916 1
< 0.1%
3.376657498 1
< 0.1%
3.380193925 1
< 0.1%
ValueCountFrequency (%)
249.7642824 1
< 0.1%
238.4159531 1
< 0.1%
235.2865871 1
< 0.1%
231.806978 1
< 0.1%
230.9718641 1
< 0.1%
229.9599727 1
< 0.1%
225.0945688 1
< 0.1%
224.3574066 1
< 0.1%
221.3408789 1
< 0.1%
218.7104612 1
< 0.1%

Wind speed (m/s)
Real number (ℝ)

Distinct52339
Distinct (%)99.7%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5.5739466
Minimum0.20463182
Maximum20.659919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:46.115977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.20463182
5-th percentile2.1290148
Q13.8133894
median5.3176898
Q36.9282094
95-th percentile10.11089
Maximum20.659919
Range20.455287
Interquartile range (IQR)3.11482

Descriptive statistics

Standard deviation2.481537
Coefficient of variation (CV)0.44520287
Kurtosis1.2237723
Mean5.5739466
Median Absolute Deviation (MAD)1.5528553
Skewness0.83842834
Sum292665.64
Variance6.158026
MonotonicityNot monotonic
2023-07-08T17:24:46.207841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.841364717 3
 
< 0.1%
5.899614882 2
 
< 0.1%
6.19865675 2
 
< 0.1%
4.874389458 2
 
< 0.1%
6.936998844 2
 
< 0.1%
5.249704647 2
 
< 0.1%
5.337876511 2
 
< 0.1%
5.63491354 2
 
< 0.1%
6.270103931 2
 
< 0.1%
6.635803699 2
 
< 0.1%
Other values (52329) 52485
99.9%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
0.2046318176 1
< 0.1%
0.206980499 1
< 0.1%
0.2098878562 1
< 0.1%
0.2607378162 1
< 0.1%
0.2880663214 1
< 0.1%
0.2883938879 1
< 0.1%
0.2932688527 1
< 0.1%
0.3154313266 1
< 0.1%
0.3167062189 1
< 0.1%
0.3243001699 1
< 0.1%
ValueCountFrequency (%)
20.65991889 1
< 0.1%
20.05744648 1
< 0.1%
19.1715611 1
< 0.1%
18.32548213 1
< 0.1%
18.28811588 1
< 0.1%
18.15354317 1
< 0.1%
17.90756631 1
< 0.1%
17.89646244 1
< 0.1%
17.8484262 1
< 0.1%
17.81222715 1
< 0.1%

Tower Acceleration y (mm/ss)
Real number (ℝ)

Distinct52488
Distinct (%)> 99.9%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean28.921473
Minimum1.6810982
Maximum148.10542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:46.301659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.6810982
5-th percentile4.6414296
Q119.276388
median27.242212
Q336.785307
95-th percentile56.250398
Maximum148.10542
Range146.42432
Interquartile range (IQR)17.508919

Descriptive statistics

Standard deviation15.939002
Coefficient of variation (CV)0.55111309
Kurtosis3.3333483
Mean28.921473
Median Absolute Deviation (MAD)8.6738727
Skewness1.1710944
Sum1518550.9
Variance254.05179
MonotonicityNot monotonic
2023-07-08T17:24:46.397089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.74675169 2
 
< 0.1%
33.0594902 2
 
< 0.1%
4.68023243 2
 
< 0.1%
23.60302279 2
 
< 0.1%
36.70997548 2
 
< 0.1%
4.47669006 2
 
< 0.1%
26.61545959 2
 
< 0.1%
26.68748093 2
 
< 0.1%
4.735356075 2
 
< 0.1%
4.46919632 2
 
< 0.1%
Other values (52478) 52486
99.9%
(Missing) 54
 
0.1%
ValueCountFrequency (%)
1.681098223 1
< 0.1%
1.95278728 1
< 0.1%
3.045718908 1
< 0.1%
3.255295557 1
< 0.1%
3.267027634 1
< 0.1%
3.30191493 1
< 0.1%
3.304721671 1
< 0.1%
3.306828921 1
< 0.1%
3.325014114 1
< 0.1%
3.412190437 1
< 0.1%
ValueCountFrequency (%)
148.1054167 1
< 0.1%
147.5075847 1
< 0.1%
138.7992899 1
< 0.1%
135.1975845 1
< 0.1%
134.9690609 1
< 0.1%
133.3424349 1
< 0.1%
132.6244801 1
< 0.1%
131.0272116 1
< 0.1%
129.9268731 1
< 0.1%
127.4053246 1
< 0.1%

Metal particle count counter
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing54
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean578.00535
Minimum564
Maximum594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:46.491063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum564
5-th percentile565
Q1573
median578
Q3584
95-th percentile591
Maximum594
Range30
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.4101127
Coefficient of variation (CV)0.012820146
Kurtosis-0.70134711
Mean578.00535
Median Absolute Deviation (MAD)5
Skewness0.0379529
Sum30348749
Variance54.90977
MonotonicityIncreasing
2023-07-08T17:24:46.570893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
575 5718
 
10.9%
581 4472
 
8.5%
570 3310
 
6.3%
588 3242
 
6.2%
577 3194
 
6.1%
584 2982
 
5.7%
578 2740
 
5.2%
574 2573
 
4.9%
564 2339
 
4.5%
573 2282
 
4.3%
Other values (21) 19654
37.4%
ValueCountFrequency (%)
564 2339
4.5%
565 991
 
1.9%
566 549
 
1.0%
567 1473
2.8%
568 227
 
0.4%
569 143
 
0.3%
570 3310
6.3%
571 1782
3.4%
572 1101
 
2.1%
573 2282
4.3%
ValueCountFrequency (%)
594 471
 
0.9%
593 350
 
0.7%
592 1226
 
2.3%
591 598
 
1.1%
590 432
 
0.8%
589 891
 
1.7%
588 3242
6.2%
587 875
 
1.7%
586 1363
2.6%
585 1796
3.4%

Interactions

2023-07-08T17:24:41.902258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:27.982329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:29.084339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:30.219705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:31.337767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:32.514652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:33.622942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:34.787907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:35.965380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:37.234591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:38.382231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:39.522249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:40.736942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:41.982973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:28.065099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:29.165483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:30.298926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:31.411612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:32.594388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:33.705950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:34.869955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:36.046597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:37.317529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:38.465235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:39.601363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:40.818446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:42.075420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:28.150191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:29.255693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:30.387131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:31.499114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:32.681374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:33.798880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:34.963658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:36.254750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:37.406576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:38.553001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:39.688894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:40.911285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:42.164999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:28.235440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:29.347732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:30.475275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:31.582339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:32.770473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:33.892769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:35.054879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:36.342735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:37.499058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:38.645705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:39.778042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:41.001324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:42.250745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:28.312421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:29.426667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:30.555830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:31.655443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:32.848485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:33.974659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:35.140826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:36.424562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:37.581275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:38.727793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:39.855965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:41.084202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:42.339432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:28.392758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:29.516045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:30.639617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:31.735855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:32.931114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:34.062660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:35.229904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:36.511064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:37.668041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:38.815387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:39.939480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:41.170989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:42.434127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:28.481251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:29.606795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:30.731532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:31.821253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:33.020686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:34.154053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:35.325701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:36.607071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:37.763189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:38.907826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:40.030777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:41.269152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:42.526852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:28.572981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:29.701846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:30.822169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:31.908848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:33.111995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:34.249906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:35.421451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:36.703048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:37.857505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:38.998910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:40.231213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:41.365649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:42.619758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:28.660486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:29.790598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:30.910587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:31.992867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:33.199244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:34.342165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:35.514834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:36.793640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:37.947997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:39.090554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:40.316926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:41.458689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:42.708540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:28.749108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:29.878977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:30.998430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:32.075107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:33.284810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:34.433914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:35.607462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:36.883839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:38.035559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:39.179351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:40.401633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:41.550000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:42.795467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:28.831711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:29.961442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:31.080549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:32.153314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:33.366942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:34.519756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:35.693823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:36.968987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:38.120436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:39.265392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:40.482524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:41.636706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:42.875449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:28.911840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:30.042326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:31.161522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:32.228280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:33.445343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:34.604548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:35.781535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:37.050742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:38.201979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:39.345999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:40.563642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:41.718845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:42.965749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:28.997197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:30.131036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:31.249406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:32.429853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:33.536985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:34.696237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:35.873192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:37.145340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:38.293588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:39.434930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:40.650188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:41.808463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-08T17:24:46.655257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Power (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
Power (kW)1.0000.0630.059-0.5680.7340.9810.981-0.1750.9460.5530.9810.754-0.003
Wind direction (°)0.0631.0000.8820.0170.0430.0910.092-0.0390.0640.2240.0520.181-0.078
Nacelle position (°)0.0590.8821.0000.0190.0350.0870.087-0.0400.0600.2210.0440.178-0.080
blade_angle-0.5680.0170.0191.000-0.614-0.542-0.5430.170-0.576-0.268-0.551-0.307-0.061
Rear bearing temperature (°C)0.7340.0430.035-0.6141.0000.7300.7280.0630.8310.4720.7180.5450.064
Rotor speed (RPM)0.9810.0910.087-0.5420.7301.0001.000-0.1670.9410.6030.9580.782-0.011
Generator RPM (RPM)0.9810.0920.087-0.5430.7281.0001.000-0.1750.9410.6030.9580.781-0.012
Nacelle ambient temperature (°C)-0.175-0.039-0.0400.1700.063-0.167-0.1751.000-0.124-0.097-0.153-0.1130.138
Front bearing temperature (°C)0.9460.0640.060-0.5760.8310.9410.941-0.1241.0000.5330.9290.7140.016
Tower Acceleration X (mm/ss)0.5530.2240.221-0.2680.4720.6030.603-0.0970.5331.0000.5050.848-0.021
Wind speed (m/s)0.9810.0520.044-0.5510.7180.9580.958-0.1530.9290.5051.0000.7200.006
Tower Acceleration y (mm/ss)0.7540.1810.178-0.3070.5450.7820.781-0.1130.7140.8480.7201.000-0.022
Metal particle count counter-0.003-0.078-0.080-0.0610.064-0.011-0.0120.1380.016-0.0210.006-0.0221.000

Missing values

2023-07-08T17:24:43.095241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T17:24:43.290156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T17:24:43.521072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
02019-01-01 00:00:00154.744492280.723969292.1686710.54499965.7200019.4215921117.8918469.38000065.56500295.3838044.16350128.246437564.0
12019-01-01 00:10:00124.603096289.953918292.1686710.74383365.3000039.4152861118.4788829.22250064.455002110.9499444.10309925.522949564.0
22019-01-01 00:20:00202.220444287.387634292.1686710.37299366.2774969.6755891149.0776379.20000065.879997106.7566684.63634431.257839564.0
32019-01-01 00:30:00172.023529293.445343292.1686710.47049665.8075039.5688241137.2458509.26000065.492500115.8023994.07011436.319843564.0
42019-01-01 00:40:0032.127945305.289215292.1686711.44333463.7724999.4328451119.2728279.30000062.665001111.4691012.96605330.062601564.0
52019-01-01 00:50:00119.129059319.532196302.6287840.71966663.9474989.4428771121.7658699.26000062.22250066.2178043.99165020.473658564.0
62019-01-01 01:00:00112.576714316.853516318.5102230.75449964.5124979.3060501105.0009779.21000063.11500278.6723403.46278525.872637564.0
72019-01-01 01:10:0047.897076317.138916318.5102231.29333663.3424999.2685981100.1422129.20249961.61000180.2839513.17939223.585058564.0
82019-01-01 01:20:0036.380810297.461761314.9500731.31867462.1074989.3350781108.4127209.11000059.67499975.8009643.42386022.610909564.0
92019-01-01 01:30:0085.886681284.841034303.0778201.04400563.0099989.3106971104.8992929.04500060.48500181.3129433.75915422.588820564.0
# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
525502019-12-31 22:20:00171.56732698.49049193.5087740.17266664.7599999.2365281097.4666257.200064.13750076.1262644.20734630.822909594.0
525512019-12-31 22:30:00166.90422193.58724193.5087740.19766564.8925009.1758771090.5664047.200064.44999992.9819434.29071019.838586594.0
525522019-12-31 22:40:00159.49348687.53380993.5087740.27166664.6100009.1025071082.4008967.080064.14250051.9959714.55801025.800391594.0
525532019-12-31 22:50:00181.13201888.31299893.5087740.02466664.5324999.0509601075.4667666.950064.07250156.9254184.70879324.872130594.0
525542019-12-31 23:00:00233.24326987.82209693.5087740.02466665.0925009.3576051111.2741856.745064.97249958.6761285.10269019.413986594.0
525552019-12-31 23:10:00237.24690687.25855493.5087740.00000065.4075009.3002301105.4420876.705065.69000057.2002795.01056426.619867594.0
525562019-12-31 23:20:00208.11649987.41149693.5087740.07399965.2150019.2671871101.9345876.700065.51750155.1739224.73712023.881981594.0
525572019-12-31 23:30:00288.90043889.80354993.5087740.00000066.3175009.8059111165.1014946.652566.76000051.8609695.51477717.420159594.0
525582019-12-31 23:40:00304.592178100.90604793.5087740.00000067.4849999.9599821183.7844316.610068.45249952.1235365.18958124.362798594.0
525592019-12-31 23:50:00268.443102111.69672893.5087740.00000066.7825009.6947311152.3739016.767567.99000086.2154774.75009529.448773594.0